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- Tasks: Build and scale data pipelines using Python and SQL while integrating AI for faster delivery.
- Company: Fast-scaling fintech with a high-performing Data & AI team.
- Benefits: Competitive salary, flexible working hours, and opportunities for rapid learning.
- Why this job: Join a dynamic team and make a real impact in the fintech space with cutting-edge technology.
- Qualifications: 2+ years in data engineering, strong Python and SQL skills, and experience with ETL/ELT pipelines.
- Other info: Collaborative environment with excellent career growth and exposure to innovative AI workflows.
The predicted salary is between 60000 - 80000 £ per year.
We’re hiring an experienced Data Engineer to join a high-performing Data & AI team within a fast-scaling fintech. This is a hands-on role building modern data products end-to-end, using AI to accelerate delivery while maintaining strong engineering standards around governance, quality and scalability.
Over the last 12 months the team has attracted exceptional engineering talent, creating an environment where you’ll learn quickly and ship meaningful work. AI is actively integrated across the organisation, and you’ll help take those use cases further building reliable foundations and turning them into production ready capabilities.
You’ll contribute to high-impact workstreams including investor-style onboarding flows, entity and account structures, and a portfolio performance monitoring product that delivers monthly updates on how assets and exposures are progressing, and then rotate across domains over time to broaden your technical depth and product understanding.
What you’ll do:
- Build and scale data pipelines and data products using Python and SQL
- Use AI-enabled approaches to deliver high-quality data solutions faster, without compromising best practice
- Own delivery within your domain: data quality, documentation, accountability and maintainability
- Translate business needs into clear technical requirements and robust data models
- Build API-based integrations with source systems and deliver analytics-ready datasets
- Help improve how the team ships: testing, CI/CD patterns, orchestration and continuous improvement
What we’re looking for:
- Strong Python and SQL (comfortable coding from scratch)
- Influence on business operations (stakeholders) and API based integrations
- Experience building reliable ETL/ELT pipelines and production data models
- Strong data modelling fundamentals and ability to communicate with stakeholders
- Exposure to AI-enabled workflows is a plus (e.g., using AI tools to accelerate engineering, or supporting AI use cases with trusted data)
- DBT experience is a big plus
- Snowflake experience is helpful (not essential)
- 2+ years’ experience in data engineering or related fields (level flexible based on impact)
Data Engineer in London employer: Siena Partnership
Contact Detail:
Siena Partnership Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the fintech space, especially those in data roles. Use platforms like LinkedIn to connect and engage with them. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those involving Python and SQL. Share it during interviews or even on your LinkedIn profile. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills. Practice common data engineering problems and be ready to discuss your past projects. We recommend using platforms like LeetCode or HackerRank to get in the zone!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Data Engineer role. Highlight your Python and SQL expertise, and any experience with AI-enabled workflows. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how your background aligns with our fintech environment. Let us know how you can help us build modern data products.
Showcase Your Projects: If you've worked on relevant projects, don’t hold back! Include links or descriptions of your work, especially if it involves building ETL/ELT pipelines or using AI tools. We love seeing practical examples of your skills in action.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen to join our team at StudySmarter!
How to prepare for a job interview at Siena Partnership
✨Know Your Tech Stack
Make sure you’re well-versed in Python and SQL, as these are crucial for the role. Brush up on your coding skills and be ready to demonstrate your ability to build data pipelines from scratch during the interview.
✨Understand the Business Context
Familiarise yourself with the fintech industry and how data engineering plays a role in it. Be prepared to discuss how you can translate business needs into technical requirements, showcasing your understanding of stakeholder communication.
✨Showcase Your AI Knowledge
Since AI is integrated across the organisation, highlight any experience you have with AI-enabled workflows. Discuss how you’ve used AI tools to enhance data solutions or improve engineering processes in your previous roles.
✨Prepare for Practical Scenarios
Expect to tackle real-world problems during the interview. Prepare to discuss your experience with building ETL/ELT pipelines and how you ensure data quality and maintainability. Practising common scenarios can help you articulate your thought process effectively.